Systems and methods for dynamic situational signal processing for target detection and classfication

ABSTRACT

Certain embodiments of the present invention provide a system for improved signal processing within a remote sensor system. The system includes a detection component and a processing component. The detection component is adapted to detect an event and generate a signal based at least in part on the event. The processing component adapted to process a signal based at least in part on a situation. Certain embodiments of the present invention provide a method for improved signal processing within a remote sensor system. The method includes determining a situation, detecting an event, generating a signal based at least in part on the event, and processing the signal based at least in part on the situational parameter.

BACKGROUND OF THE INVENTION

The present invention generally relates to remote sensor systems. Morespecifically, the present invention relates to systems and methods forimproved data communications and/or improved signal processing withinremote sensor systems.

Throughout the world, military and homeland security forces face anincreasing need to provide safety and security to troops and high valueassets. Remote sensor systems are ideal for surveillance and monitoringof high-value assets, such as troop encampments, airfields, baseinstallations, supply routes, and depots. In larger networks, remotesensor systems are used to monitor and protect national borders,regional boundaries, and assets in homeland defense and peacekeepingoperations.

Remote sensor systems typically include a network of easily deployed,remotely located sensors that detect the movement of personnel andvehicles. These sensors are typically remote, battery-operated devicesthat provide commanders with critical surveillance data on a 24-hourbasis.

Existing remote sensor systems include several disadvantages. Forexample, existing remote sensor systems typically flood the network withraw data to be later interpreted at a control station. Additionally,these systems generate a large number of false alarms due to uneventfuldetections, such as animal movement near the sensors. As anotherexample, existing remote sensor systems are typically designed for aspecific application, yet lack sufficient battery life to last theentire mission.

Thus, there is a need for systems and methods for improved datacommunications and/or improved signal processing within remote sensorsystems.

BRIEF SUMMARY OF THE INVENTION

Certain embodiments of the present invention provide a system forimproved signal processing within a remote sensor system. The systemincludes a detection component and a processing component. The detectioncomponent is adapted to detect an event and generate a signal based atleast in part on the event. The processing component adapted to processa signal based at least in part on a situation.

Certain embodiments of the present invention provide a method forimproved signal processing within a remote sensor system. The methodincludes determining a situation, detecting an event, generating asignal based at least in part on the event, and processing the signalbased at least in part on the situational parameter.

Certain embodiments of the present invention provide a computer readablestorage medium. The computer readable storage medium includes a set ofinstructions for execution on a computer. The set of instructionsincludes a detection routine and a processing routine. The detectionroutine is configured to detect an event and generate a signal based atleast in part on the event. The processing routine is configured toprocess the signal based at least in part on a situation.

BRIEF DESCRIPTION OF SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 illustrates an exemplary remote sensor system 100 operating inaccordance with an embodiment of the present invention.

FIG. 2 illustrates an exemplary remote sensor system 200 operating inaccordance with an embodiment of the present invention.

FIG. 3 illustrates an exemplary remote sensor system 300 operating inaccordance with an embodiment of the present invention.

FIG. 4 illustrates a system 400 for improved data communications withina remote sensor system according to an embodiment of the presentinvention.

FIG. 5 illustrates an example 500 of a sensor transmission ruleoperating in accordance with an embodiment of the present invention.

FIG. 6 illustrates an example 600 of a gateway transmission ruleoperating in accordance with an embodiment of the present invention.

FIG. 7 illustrates a method 700 for improved data communications withina remote sensor system according to an embodiment of the presentinvention.

FIG. 8 illustrates a system 800 for improved signal processing within aremote sensor system according to an embodiment of the presentinvention.

FIG. 9 illustrates an exemplary signal processing system 900 operatingin accordance with an embodiment of the present invention.

FIG. 10 illustrates a method 1000 for improved signal processing withina remote sensor system according to an embodiment of the presentinvention.

The foregoing summary, as well as the following detailed description ofcertain embodiments of the present invention, will be better understoodwhen read in conjunction with the appended drawings. For the purpose ofillustrating the invention, certain embodiments are shown in thedrawings. It should be understood, however, that the present inventionis not limited to the arrangements and instrumentality shown in theattached drawings.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 illustrates an exemplary remote sensor system 100 operating inaccordance with an embodiment of the present invention. For example, thesystem 100 may include a RF-5400 sensor system (Harris Corporation,Rochester, N.Y.). The system 100 includes one or more sensor nodes 110,a control center node 120, and one or more communication links 130. Inoperation, one or more events 140, such as vehicles and personnel, maybe detected by sensor nodes 110. For example, sensor node 110 mayinclude a RF-5400VH-SS miniature sensor, a RF-5400VH-MS multiple sensor,and a RF-5400V-SR sensor/relay, as shown in FIG. 1. The events 140 maybe communicated to control center node 120, for example, via radiofrequency communication links 130. The control center node 120 mayinclude, for example, a Falcon II handheld radio, also shown in FIG. 1.Alternatively and/or in addition, one or more commands (e.g., a commandset) may be may be communicated, for example, from the control centernode 120 to the sensor node 110, to provide “on the fly”re-configuration the system 100.

FIG. 2 illustrates an exemplary remote sensor system 200 operating inaccordance with an embodiment of the present invention. For example, thesystem 200 may include a RF-5400 sensor system (Harris Corporation,Rochester, N.Y.). The system 200 includes a plurality of sensor nodes210, a plurality of control center nodes 220, and a plurality ofcommunication links 230. In operation, one or more events 240, such asvehicles and personnel, may be detected by sensor nodes 210. Forexample, sensor nodes 210 may include a RF-5400VH-SS miniature sensorand an RF-5400V-SR sensor/relay, as shown in FIG. 2. The events 240 maybe communicated to control center nodes 220, for example, via radiofrequency communication links 230. The control center nodes 220 mayinclude, for example, a Falcon II handheld radio, a RF-5800V-MP manpackradio, a RF-5410 sensor management application, and a RF-6910situational awareness application, also shown in FIG. 2. Alternativelyand/or in addition, one or more commands (e.g., a command set) may bemay be communicated, for example, from the control center nodes 220 tothe sensor nodes 210, to provide “on the fly” re-configuration thesystem 200.

FIG. 3 illustrates an exemplary remote sensor system 300 operating inaccordance with an embodiment of the present invention. For example, thesystem 300 may include a RF-5400 sensor system (Harris Corporation,Rochester, N.Y.). The system 300 includes a plurality of sensor nodes310, a plurality of gateway nodes 315, a control center node 320, and aplurality of communication links 330. In operation, one or more events340, such as vehicles and personnel, may be detected by sensor nodes310. For example, sensor nodes 310 may include a RF-5400VH-SS miniaturesensor and a RF-5400VH-MS multiple sensor, as shown in FIG. 3. Theevents 340 may be communicated to gateway nodes 315, for example, viaradio frequency communication links 330. The gateway nodes 315 mayinclude, for example, a RF-5400VH-RU relay and a RF-5400VH-GWintelligent gateway, also shown in FIG. 3. The events 340 may becommunicated to control center node 320, for example, via satellitecommunication links 330. The control center node 320 may include, forexample, a RF-5410 sensor management application and a RF-6910situational awareness application, also shown in FIG. 3. Alternativelyand/or in addition, one or more commands (e.g., a command set) may bemay be communicated, for example, from the control center node 320 tothe gateway nodes 315 and/or the sensor nodes 310, to provide “on thefly” re-configuration the system 300.

FIG. 4 illustrates a system 400 for improved data communications withina remote sensor system according to an embodiment of the presentinvention. For example, the system 400 may include a RF-5400 sensorsystem (Harris Corporation, Rochester, N.Y.). The system 400 includes asensor layer 410, a gateway layer 420, and control center layer 430,which are described in more detail below.

The sensor layer 410 may include one or more sensor nodes 415. Forexample, the sensor nodes 415 may include sensors, such as RF-5400VH-SSminiature sensors, RF-5400VH-MS multiple sensors, and/or RF-5400V-SRsensors/relays. The sensor nodes 415 may also include, for example,detectors, such as seismic, acoustic, magnetic, and/or passive infra-red(PIR) detectors.

The gateway layer 420 may include one or more gateway nodes 425. Forexample, the gateway nodes 425 may include range extenders, such asRF-5400VH-RU relays, RF-5400V-SR sensor/relays, and/or RF-5400VH-GWintelligent gateways.

The control center layer 430 may include one or more control centernodes 435. For example, the control center node 435 may includemonitors, such as Falcon II handheld radios, RF-5800V-MP manpack radios,RF-5410 sensor management applications, and/or RF-6910 situationalawareness applications.

The sensor layer 410 is in communication with the gateway layer 420and/or the control center layer 430. For example, as shown in FIG. 4, aplurality of sensor nodes 415 may communicate directly with a gatewaynode 425 and indirectly with a control center node 435. As anotherexample, the plurality of sensor nodes 415 may communicate directly withthe control center node 435.

The gateway layer 420 is in communication with the sensor layer 410and/or the control center layer 430. For example, as shown in FIG. 4, agateway node 425 may communicate with a plurality of sensor nodes 415and a plurality of gateway nodes 425 may communicate with a controlcenter node 435.

The control center layer 430 is in communication with the sensor layer410 and/or the gateway layer 420. For example, as shown in FIG. 4, acontrol center node 435 may communicate directly with a plurality ofgateway nodes 425 and indirectly with a plurality of sensor nodes 415.

The sensor layer 410 is adapted to detect one or more events. Forexample, a seismic detector in sensor node 415 may be adapted to detectmovement of personnel. As another example, a passive infra-red (PIR)detector in sensor node 410 may be adapted to detect left to rightmovement of vehicles.

The sensor layer 410 is adapted to generate data based at least in parton one or more events. The data may include, for example, data, signals,events, and/or reports. The data may be stored, for example, in adatabase. The database may be indexed, for example, based at least inpart on network identification (network ID), cluster area, time,classification, direction, global positioning satellite location (GPSlocation), and/or detection type.

The sensor layer 410 is adapted to process and/or communicate data basedat least in part on one or more rules and/or algorithms (e.g., a rule oralgorithm set). For example, a plurality of sensor nodes 415 may beadapted to transmit data to a gateway node 425 and/or a control centernode 435 based at least in part on a sensor transmission rule. The ruleset may include, for example, ordered/unordered events, directionspecific events, and/or classification specific events. The rule set maybe configured “on the fly”, for example, by the system 400 and/or a userof the system 400. The rule set may be configured remotely, for example,from any node in the system 400.

FIG. 5 illustrates an example 500 of a sensor transmission ruleoperating in accordance with an embodiment of the present invention. Theexample 500 includes a data transmission sequence 510 and a datahold/drop sequence 520. The data transmission sequence 510 includes asliding association window 515. The example 500 also includes data 530,which may include, for example, data, signals, events, and/or reports.The example 500 is described with reference to the system 400 of FIG. 4,but it should be understood that other implementations are possible.

In operation, the data 530 is compared to the sensor transmission rule.If the rule is satisfied, then the data 530 may be communicated.Alternatively and/or in addition, a single event corresponding to therule may be transmitted to reduce the amount of data transmitted andstill indicate that the rule has been satisfied. Conversely, if the ruleis not satisfied, then the data 530 may be held and/or dropped.

For example, RULE X may be configured by a user to be a combination ofEVENT A, EVENT B, and EVENT C in the order of A>C>B within 60 seconds,where EVENT A includes passive infra-red (PIR) detection of a relativeleft to right moving target, EVENT B includes seismic detection of anyclassification, and EVENT C includes magnetic “tripwire” detection. Thedata 530 in the sliding association window 515 of the data transmissionsequence 510 satisfies RULE X, and consequently, may be transmitted, forexample, from a sensor node 415 to a gateway node 425 and/or a controlcenter node 435. Alternatively and/or in addition, a single eventcorresponding to RULE X may be transmitted to reduce the amount of datatransmitted and still indicate that RULE X has been satisfied.Conversely, the data 530 in the data hold/drop sequence 520 does notsatisfy RULE X, and consequently, may be held and/or dropped.

Although the data transmission sequence 510 is described with referenceto a single sliding association rule 515, it should be understood thatone or more sliding association windows 515 are possible. For example,if a second start condition is present, such as EVENT A, then a secondsliding association window 515 may be started. If EVENT C and then EVENTB follow within 60 seconds of EVENT A, then RULE X is satisfied and thedata 530 in the second sliding association window 515 may betransmitted, for example, from the sensor node 415 of FIG. 4 to agateway node 425 and/or a control center node 435. Conversely, if EVENTC and then EVENT B do not follow within 60 seconds of EVENT A, then RULEX is not satisfied, the second sliding window 515 may be closed, and thedata 530 in the second sliding association window 515 may be held and/ordropped.

The gateway layer 420 is adapted to process and/or communicate databased at least in part on one or more rules and/or algorithms (e.g., arule or algorithm set). For example, a gateway node 425 may be adaptedto receive data from one or more sensor nodes 415 and transmit the datato a control center node 435 based at least in part on a gatewaytransmission rule. The rule set may include, for example,ordered/unordered events, node identification (node ID) based events,and/or report type based events. The rule set may be configured “on thefly”, for example, by the system 400 and/or a user of the system 400.The rule set may be configured remotely, for example, from any node inthe system 400.

FIG. 6 illustrates an example 600 of a gateway layer rule operating inaccordance with an embodiment of the present invention. The example 600includes a data transmission sequence 610 and a data hold/drop sequence620. The data transmission sequence 610 includes a sliding associationwindow 615. The example 600 also includes data 630, which may include,for example, data, signals, events, and/or reports.

In operation, the data 630 is compared to the sensor layer rule. If therule is satisfied, then the data 630 may be transmitted. Alternativelyand/or in addition, a single event corresponding to the rule may betransmitted to reduce the amount of data transmitted and still indicatethat the rule has been satisfied. Conversely, if the rule is notsatisfied, then the data 630 may be held and/or dropped.

For example, RULE Y may be configured by a user to include a combinationof SENSOR NODE 21, SENSOR NODE 22, and SENSOR NODE 23, each sensor nodereporting identical events in the order 21>22>23 within 180 seconds. Thedata 630 in the sliding association window 615 of the data transmissionsequence 610 satisfies RULE Y, and consequently, may be transmitted, forexample, from a gateway node 425 to a control center node 435.Alternatively, a single event corresponding to RULE Y may be transmittedto reduce the amount of data transmitted and still indicate that RULE Yhas been satisfied. Conversely, the data 630 in the data hold/dropsequence 620 does not satisfy RULE Y, and consequently, may be heldand/or dropped.

Although the data transmission sequence 610 is described with referenceto a single sliding association rule 615, it should be understood thatone or more sliding association windows 615 are possible. For example,if a second start condition is present, such as SENSOR NODE 21 reportingan event, then a second sliding association window 615 may be started.If SENSOR NODE 22 and then SENSOR NODE 23 report the same event within180 seconds, then RULE Y is satisfied, and consequently, the data 630 inthe second sliding association window 615 may be transmitted, forexample, from a gateway node 425 to a control center node 435.Conversely, if SENSOR NODE 22 and then SENSOR NODE 23 do not report thesame event within 180 seconds, then RULE Y is not satisfied, the secondsliding association window 615 may be closed, and the data 630 in thesecond sliding association widow 615 may be held and/or dropped.

The control center layer 430 is adapted to notify a user based at leastin part on one or more rules and/or algorithms (e.g., a rule oralgorithm set). For example, a control center node 435 may receive datafrom a plurality of sensor nodes 415 and/or a plurality of gateway nodes425 and automatically alert a user when the data matches a patternrecognition template. The rule set may include, for example,ordered/unordered events and/or analog/digital signatures. The rule setmay be implemented using a sliding association window, as describedabove. The rule set may be configured “on the fly”, for example, by thesystem 400 and/or a user of the system 400. The rule set may beconfigured remotely, for example, from any node in the system 400.

For example, a user may create a pattern recognition template thatmatches on relative left to right movement of a vehicle reported firstfrom SENSOR NODE A and then from SENSOR NODE B within 5 minutes. Thecontrol center layer 430 may query a database and alert the user whenthe data in the database matches the pattern recognition template.

The rule set may be run continuously or periodically (e.g., daily,hourly, etc.) depending on the particular application of the system 400,such as force protection, perimeter surveillance, and/or remote boardermonitoring.

As discussed above, the components, elements, and/or functionality ofthe system 400 may be implemented alone or in combination in variousforms in hardware, firmware, and/or as a set of instructions insoftware, for example. Certain embodiments may be provided as a set ofinstructions residing on a computer-readable medium, such as a memory,hard disk, DVD, or CD, for execution on a general purpose computer orother processing device.

FIG. 7 illustrates a method 700 for improved data communications withina remote sensor system according to an embodiment of the presentinvention. The method 700 includes the following steps, which aredescribed in more detail below. At step 710, an event is detected. Atstep 720, data is generated. At step 730, the data is processed. At step740, the data is communicated. At step 750, a user is notified. Themethod 700 is described with reference to the components of the system400 of FIG. 4, but it should be understood that other implementationsare possible.

At step 710, an event is detected, for example, by a sensor node, suchas sensor node 415. The event may include, for example, left to rightvehicle movement detected by a passive infra-red (PIR) detector.

At step 720, data is generated based at least in part on the event. Forexample, the left to right vehicle movement detected by the passiveinfra-red (PIR) detector may be reported in a database.

At step 730, the data is processed based at least in part on a rule. Forexample, if the rule is satisfied (e.g., the data matches the rule),then the data may be transmitted. Conversely, for example, if the ruleis not satisfied (e.g., the data does not match the rule), then the datamay be held and/or dropped. The rule may include, for example, a sensortransmission rule, a gateway transmission rule, and/or a control centernotification rule.

At step 740, the data is communicated. For example, the data may betransmitted by the sensor node and received by a gateway node, such asgateway node 425, and/or a control center node, such as control centernode 435. As another example, the data may be transmitted by the gatewaynode and received by the control center node.

In certain embodiments of the present invention, the data may becommunicated when the rule is satisfied. For example, the data may betransmitted from the sensor node to the gateway node and/or the controlcenter node when a sensor transmission rule is satisfied. As anotherexample, the data may be transmitted from the gateway node to thecontrol center node when a gateway rule is satisfied.

At step 750, a user is notified when the control center notificationrule is satisfied. For example, a user may be automatically alerted whendata in a database matches a pattern recognition template.

One or more of the steps 710-750 of the method 700 may be implementedalone or in combination in hardware, firmware, and/or as a set ofinstructions in software, for example. Certain embodiments may beprovided as a set of instructions residing on a computer-readablemedium, such as a memory, hard disk, DVD, or CD, for execution on ageneral purpose computer or other processing device.

Certain embodiments of the present invention may omit one or more ofthese steps and/or perform the steps in a different order than the orderlisted. For example, some steps may not be performed in certainembodiments of the present invention. As a further example, certainsteps may be performed in a different temporal order, includingsimultaneously, than listed above.

FIG. 8 illustrates a signal processing system 800 according to anembodiment of the present invention. For example, the system 800 may beimplemented as part of a sensor system, such as the sensor system 400 ofFIG. 4. The system 800 includes a detection component 810, a processingcomponent 820, and analysis component 830, a classification component840, and a communication component 850. The system 800 is described withreference to the components of the sensor system 400 of FIG. 4, but itshould be understood that other implementations are possible.

The detection component 810 is adapted to detect one or more events,such as movement of personnel and/or vehicles. The detection component810 may include detectors, such as seismic, acoustic, magnetic, and/orpassive infra-red (PIR) detectors. For example, a seismic detector in asensor node, such as sensor node 415 of FIG. 4, may communicate a signalthat can be later interpreted to be personnel and/or vehicles. Asanother example, a passive infra-red (PIR) detector in a sensor node,such as sensor node 415 of FIG. 4, may communicate a signal that can belater interpreted to be directional and/or tripline.

The detection component 810 is adapted to generate a signal based atleast in part on the event. For example, a seismic detector may detectseismic vibrations and generate a corresponding electrical signal. Asanother example, a passive infra-red (PIR) detector may detect infra-redenergy and generate a corresponding electrical signal.

The processing component 820 is adapted to process a signal. Forexample, the processing component 820 may be adapted to process thesignal generated by the detection component 810.

In certain embodiments of the present invention, the processingcomponent 820 may be adapted to process the signal based at least inpart on a situation. FIG. 9 illustrates an exemplary signal processingsystem 900 operating in accordance with an embodiment of the presentinvention. As shown in FIG. 9, for example, the signal may be processedby a bandpass filter component 901 and/or stored in a signal buffer 902based at least in part on the situation. That is, processing parameters,such as filter bandwidth, may be determined based at least in part onthe situation. Table 1, as provided below, includes several examples ofsituations and corresponding processing parameters.

TABLE 1 Examples of Situations and Corresponding ParametersProcessing/Analysis Situation Parameters Classification ParametersEnvironment: Rain Filter taps N/A Detection Threshold Environment:Smooth road Filter taps Envelope frequency Target: Pedestrian andVehicle Detection Threshold veh freq mask A Detection Pulse length Pedgait frequency test Detection Pulse spacing Time series of envelopeInput Freq veh freq mask B Target: Pedestrian Filter taps Envelopefrequency Detection Threshold Ped gait frequency test Detection Pulselength Time series of envelope Detection Pulse spacing KurtosisEnvironment: Rough road Filter taps Envelope frequency Target: vehicleDetection Threshold veh freq mask C Detection Pulse length Input Freqveh freq mask D

The situation may include, for example, a target type, such as anaircraft, a vehicle, a pedestrian, and/or an animal. As another example,the situation may include an environment or an environmental condition,such as a surface (e.g., smooth, rough, dirt, gravel, paved, loose,packed, etc.) and/or a surrounding area (e.g., plains, forests, hills,mountains, etc.). As another example, the situation may include adynamic environment or a dynamic environmental condition, such as aweather condition (e.g., rain, snow, wind, etc.) and/or a militaryactivity (e.g., heavy artillery fire, bombardment, etc.).

In certain embodiments of the present invention, the situation may bedetermined, for example, by a user. For example, a user may input asituation using an input device, such as a keyboard, a mouse, and/or atouchscreen. As another example, a user may select a situation from aplurality of available situations, for example, by selecting thesituation from a drop- or pull-down menu using a mouse and/or selectinga checkbox associated with the situation on a touchscreen. Alternativelyand/or in addition, the situation may be determined automatically, forexample, by the system 800.

Thus, certain embodiments of the present invention provide systems andmethods for adaptive and/or dynamic signal processing. That is, a signalmay be processed, for example, based at least in part on a situation, asdescribed above. An example of adaptive signal processing is providedbelow.

Example: Adaptive Signal Processing

Situation: Searching for vehicles

Environment: Paved road through a wooded area

Dynamic Environmental Condition: Rain

When the rain begins, seismic detectors receive large interferingimpulses. The system 800 detects the environmental change and adapts theinput bandpass filter to notch out those components, resulting in a“cleaner” signal (e.g., better signal to noise and interference ratio),and in turn, improved detection and classification probability. When therain stops, the adaptive front-end filter returns to the normalfrequency response.

In certain embodiments of the present invention, the processingcomponent 830 may be adapted to determine the signal envelope based atleast in part on the situation. For example, as shown in FIG. 9, thesignal envelope may be determined by a magnitude component 903 and afilter and decimate component 904 and stored in a signal envelope buffercomponent 905 based at least in part on the situation. That is,processing parameters, such as filter bandwidth, may be determined basedat least in part on the situation. Table 1, as provided above, includesseveral examples of situations and corresponding processing parameters.

In certain embodiments of the present invention, the processingcomponent 820 may be adapted to process and/or analyze the signalenvelope based at least in part on the situation. For example, as shownin FIG. 9, the signal envelope may be processed by the mean and standarddeviation component 906, the average component 906, the calculationthreshold component 908, the comparison component 909, and theassociation window component 910 based at least in part on thesituation. That is, processing/analysis parameters, such as detectionthreshold, detection pulse length, detection pulse spacing, detectiontime window length, number of pulses in the detection time window,association window length, and/or association window N of M threshold,may be determined based at least in part on the situation. Table 1, asprovided above, includes several examples of situations andcorresponding processing/analysis parameters.

The analysis component 830 is adapted to analyze a signal. For example,the analysis component 830 may be adapted to analyze the signalgenerated by the detection component 810. As another example, theanalysis component 830 may be adapted to analyze the signal processed bythe processing component 820.

In certain embodiments of the present invention, the analysis component820 may be adapted to analyze the signal, for example, in one or moredomains. For example, as shown in FIG. 9, the signal stored in thesignal buffer 902 may be analyzed in the frequency domain (e.g., orderedranking of peaks, frequency sub-band sums, desired fundamental andharmonic association filters) by the frequency analysis component 914.As another example, the signal may be analyzed in the time domain (e.g.,kurtosis, cadence analysis) by the time analysis component 915.

In certain embodiments of the present invention, the analysis component830 may be adapted to analyze the signal envelope, for example, in oneor more domains. For example, as shown in FIG. 9, the signal envelopestored in the signal envelope buffer 905 may be analyzed in thefrequency domain (e.g., ordered ranking of peaks, frequency sub-bandsums, desired fundamental and harmonic association filters) by thefrequency analysis component 912. As another example, the signalenvelope may be analyzed in the time domain (e.g., kurtosis, cadenceanalysis) by the time analysis component 913.

In certain embodiments of the present invention, the analysis component830 may be adapted to process and/or analyze the signal envelope basedat least in part on the situation. For example, as shown in FIG. 9, thesignal envelope may be analyzed by the pulse analysis component 911based at least in part on the situation. That is, processing/analysisparameters, such as detection threshold, detection pulse length,detection pulse spacing, detection time window length, number of pulsesin the detection time window, association window length, and/orassociation window N of M threshold, may be based at least in part onthe situation. Table 1, as provided above, includes several examples ofsituations and corresponding processing/analysis parameters.

Thus, certain embodiments of the present invention provide systems andmethods for envelope signal processing. That is, the envelope of asignal may be processed and/or analyzed, for example, based at least inpart on the situation, as described above. An example of envelope signalprocessing is provided below.

Example: Envelope Signal Processing

For a seismic signal, the comparison between the low frequencycomponents and the rest of the frequency components of the envelope canbe used as a feature to help determine the difference between impulsesources such as a person or animal and constant sources such as avehicle. Using the envelope of the signal allows for lower samplingrates than processing the original signal. This in turn reduces memory,processor loading, and power requirements. These comparisons include butare not limited to ratio between the low frequency components and therest of the frequency components, the power in the low frequencycomponents and the total signal power, and/or the difference betweenselect groups of frequency components.

The classification component 840 is adapted to classify an event. Forexample, the event may be classified as a pedestrian, a vehicle, a lightvehicle, a heavy vehicle, a wheeled vehicle, a tracked vehicle, and/oranother appropriate classification. The classification may include data,signals, events, and/or reports.

In certain embodiments of the present invention, the classificationcomponent 840 may be adapted to classify the event based at least inpart on a situation. For example, as shown in FIG. 9, a classificationcomponent 916 may be adapted to actuate one or more switches 917 basedat least in part on the situation. That is, classification parameters,such as feature selection and/or feature execution order, may be basedat least in part on the situation. Table 1, as provided above, includesseveral examples of situations and corresponding classificationparameters.

Thus, certain embodiments of the present invention provide systems andmethods for multi-domain signal processing. That is, a signal and/or asignal envelope may be analyzed, for example, in more than one domain.An example of multi-domain signal processing is provided below.

Example: Multi-Domain Signal Processing

Use of features from different domains to classify a target

Situation: Searching for pedestrian and vehicles

Environment: Packed soil

Features used:

Domain: Frequency spectrum of the envelope

Feature: Existence of a peak at the gait rate of a person's walk and thefirst harmonic.

Reason: This feature will detect the presence of impulses that occur atthe rate of a person's feet impact with the ground.

Domain: time series of the envelope

Feature: Kurtosis

Reason: This feature measures the “peakedness” of the probabilitydistribution. This measurement will be different for a vehicle verses apedestrian.

Domain: Frequency domain of the input digital samples

Feature: High frequency band power

Reason: This feature measures the presence of a frequency source that istypically higher then the resonant frequency provide from the impact ofa person's step.

By themselves these features are not reliable enough to accuratelypredict the presence of a person verses a vehicle. However, when theexpected results of these features are combined, a better estimate ofthe target is achieved. By using features from different domains, theinterdependency between the features measured is much smaller then usingall features from the same domain.

Additionally and/or alternatively, certain embodiments of the presentinvention provide systems and methods for power efficient signalprocessing. That is, a signal and/or a signal envelope may be processed,analyzed, and/or classified based at least in part on a limited set offeatures. An example of power efficient signal processing is providedbelow.

Example: Power Efficient Signal Processing

Not using all the features all the time.

Situation: Searching for pedestrian

Environment: Packed soil

Features used:

Domain: Frequency spectrum of the envelope

Feature: Existence of a peak at the gait rate of a person's walk and thefirst harmonic.

Reason: This feature will detect the presence of impulses that occur atthe rate of a person's feet impact with the ground.

Domain: time series of the envelope

Feature: Kurtosis

Reason: This feature measures the “peakedness” of the probabilitydistribution. This measurement will be different for background noiseand other man made sources verses a pedestrian walking.

When searching for a pedestrian against background noise, only the gaitof the footstep and the Kurtosis need to be used. If the gait rate isfound along with a harmonic to that rate, there is a high confidencethat it is a person that is walking past the sensor. A detection of apedestrian is declared. If the values for the gait fundamental andharmonic are not sufficient for a confident decision, the Kurtosis iscalculated. If the value of the Kurtosis combined with the gaitfrequency is sufficient, a pedestrian is declared. Otherwise it isassumed that the source was not a person.

Based on the confidence level of the classification from one or multipleof the features, the signal processing can either continue evaluatingfeatures or stop and report the classification. This reduces theprocessing requirements as well as the overall average systemclassification delay for resource limited remote sensors.

Additionally and/or alternatively, certain embodiments of the presentinvention provide systems and methods for multi-observation signalprocessing. That is, more than one signal may be processed, analyzed,and/or classified, as described above. An example of multi-observationsignal processing is provided below.

Example: Multi-Observation Signal Processing

Using multiple observations of the same features to classify a target.

Situation: Searching for pedestrian and vehicles

Environment: Packed soil

Features used:

Domain: Frequency spectrum of the envelope

Feature: Existence of a peak at the gait rate of a person's walk and thefirst harmonic.

Reason: This feature will detect the presence of impulses that occur atthe rate of a person's feet impact with the ground.

Domain: time series of the envelope

Feature: Kurtosis

Reason: This feature measures the “peakedness” of the probabilitydistribution. This measurement will be different for a vehicle verses apedestrian.

Domain: Frequency domain of the input digital samples

Feature: High frequency band power

Reason: This feature measures the presence of a frequency source that istypically higher then the resonant frequency provide from the impact ofa person's step.

The classification results of the features calculated and a probabilityof a pedestrian and vehicle are estimated. If the probability of eitherthe pedestrian or the vehicle is not sufficient to make a decision, thesame features are calculated again after a given delay in time. Theseprobabilities are combined with the first observation time instance.This is repeated until either, the signal goes away or the probabilitythreshold (or simple voting) of a pedestrian or vehicle is exceed.

The system performs a classification decisions based on but not limitedto one feature set or a multiple of feature sets. The decision can be ahard decision for one of the possible classification hypotheses or asoft decision where a weight or probability is assigned to each possibledecision hypothesis. The classification decision hypotheses are given aweight value. This weight value can be but not limited to a measure ofthe signal to noise ratio, confidence level, signal energy, or a simplevalue of one or zero. Over multiple observation periods, the weightedclassification decisions are combined for the possible decisionhypotheses. The combination of weighted decision values over differentobservation windows can be for a fixed duration of observation windows,until a difference threshold between classification hypotheses isachieved, or until the target exits the sensor range limits.

The communication component 850 is adapted to communicate aclassification. For example, the communication component 850 may beadapted to transmit a classification data and/or a classification reportfrom a sensor node, such as sensor node 415 of FIG. 4, to a gatewaynode, such as gateway node 425 of FIG. 4, and/or a control center node,such as control center node 435 of FIG. 4.

In certain embodiments of the present invention, the communicationcomponent 850 may be adapted to communicate with and/or facilitatecommunications between the other components 810-840 of the system 800.For example, the detection component 810 may communicate directly withthe processing component 820. As another example, the detectioncomponent 810 may communicate indirectly with the processing component820 via the communication component 850. That is, all of the components810-850 of the system 800 may communicate directly with each otherand/or indirectly via the communication component 850, as describedabove.

As discussed above, the components, elements, and/or functionality ofthe system 800 may be implemented alone or in combination in variousforms in hardware, firmware, and/or as a set of instructions insoftware, for example. Certain embodiments may be provided as a set ofinstructions residing on a computer-readable medium, such as a memory,hard disk, DVD, or CD, for execution on a general purpose computer orother processing device.

FIG. 10 illustrates a method 1000 for improved signal processing withina remote sensor system according to an embodiment of the presentinvention. At step 1010, a situation is determined. At step 1020, anevent is detected. At step 1030, a signal is generated. At step 1040,the signal is processed. At step 1050, the signal is analyzed. At step1060, the event is classified. At step 1070, the event is communicated.The method 1000 includes the following steps, which are discussed inmore detail below. The method 1000 is described with respect to thesystem 800, but it should be understood that other implementations arepossible.

At step 1010, a situation is determined. For example, the situation mayinclude a target type, an environment or environmental condition, and/ora dynamic environment or a dynamic environmental condition. Thesituation may be determined, for example, by the system 800 and/or by auser, as described above.

At step 1020, an event is detected, for example, by a detectioncomponent, such as detection component 810. For example, a vehicle maybe detected by a seismic detector. At step 1030, a signal is generatedby the detection component based at least in part on the event. Forexample, the seismic detector may generate an electrical signal from theseismic vibrations of the vehicle.

At step 1040, the signal is processed, for example, by a processingcomponent, such as processing component 820. The signal may include, forexample, the signal generated at step 1030. In certain embodiments ofthe present invention, the signal may be processed based at least inpart on the situation. In certain embodiments of the present invention,the signal may be processed to determine a signal envelope. The signalenvelope may be further processed based at least in part on thesituation.

At step 1050, the signal is analyzed, for example, by an analysiscomponent, such as analysis component 830. The signal may include, forexample, the signal generated at step 1030 and/or the signal processedat step 1040. In certain embodiments of the present invention, thesignal and/or the signal envelope may be analyzed, for example, in oneor more domains. In certain embodiments of the present invention, thesignal envelope may be analyzed based at least in part on the situation.

At step 1060, an event is classified, for example, by a classificationcomponent, such as classification component 840. For example, an impulsesource may be classified as a person, whereas a constant source may beclassified as a vehicle.

At step 1070, the classification is communicated, for example, by acommunication component, such as communication component 850.

One or more of the steps 1010-1070 of the method 1000 may be implementedalone or in combination in hardware, firmware, and/or as a set ofinstructions in software, for example. Certain embodiments may beprovided as a set of instructions residing on a computer-readablemedium, such as a memory, hard disk, DVD, or CD, for execution on ageneral purpose computer or other processing device.

Certain embodiments of the present invention may omit one or more ofthese steps and/or perform the steps in a different order than the orderlisted. For example, some steps may not be performed in certainembodiments of the present invention. As a further example, certainsteps may be performed in a different temporal order, includingsimultaneously, than listed above.

In one embodiment of the present invention, a system for improved signalprocessing within a remote sensor system includes a detection componentand a processing component. The detection component is adapted to detectan event and generate a signal based at least in part on the event. Theprocessing component adapted to process a signal based at least in parton a situation.

In one embodiment of the present invention, a method for improved signalprocessing within a remote sensor system includes determining asituation, detecting an event, generating a signal based at least inpart on the event, and processing the signal based at least in part onthe situational parameter.

In one embodiment of the present invention, a computer readable storagemedium includes a set of instructions for execution on a computer. Theset of instructions includes a detection routine and a processingroutine. The detection routine is configured to detect an event andgenerate a signal based at least in part on the event. The processingroutine is configured to process the signal based at least in part on asituation.

Thus, certain embodiments of the present invention provide systems andmethods for improved data communication and/or improved signalprocessing within a remote sensor system. Certain embodiments of thepresent invention provide the technical effect of improved datacommunication and/or improved signal processing within a remote sensorsystem.

Several embodiments are described above with reference to drawings.These drawings illustrate certain details of specific embodiments thatimplement the systems and methods and programs of the present invention.However, describing the invention with drawings should not be construedas imposing on the invention any limitations associated with featuresshown in the drawings. The present invention contemplates methods,systems and program products on any machine-readable media foraccomplishing its operations. As noted above, the embodiments of thepresent invention may be implemented using an existing computerprocessor, or by a special purpose computer processor incorporated forthis or another purpose or by a hardwired system.

As noted above, embodiments within the scope of the present inventioninclude program products comprising machine-readable media for carryingor having machine-executable instructions or data structures storedthereon. Such machine-readable media can be any available media that canbe accessed by a general purpose or special purpose computer or othermachine with a processor. By way of example, such machine-readable mediamay comprise RAM, ROM, PROM, EPROM, EEPROM, Flash, CD-ROM or otheroptical disk storage, magnetic disk storage or other magnetic storagedevices, or any other medium which can be used to carry or store desiredprogram code in the form of machine-executable instructions or datastructures and which can be accessed by a general purpose or specialpurpose computer or other machine with a processor. When information istransferred or provided over a network or another communicationsconnection (either hardwired, wireless, or a combination of hardwired orwireless) to a machine, the machine properly views the connection as amachine-readable medium. Thus, any such a connection is properly termeda machine-readable medium. Combinations of the above are also includedwithin the scope of machine-readable media. Machine-executableinstructions comprise, for example, instructions and data which cause ageneral purpose computer, special purpose computer, or special purposeprocessing machines to perform a certain function or group of functions.

Embodiments of the invention are described in the general context ofmethod steps which may be implemented in one embodiment by a programproduct including machine-executable instructions, such as program code,for example in the form of program modules executed by machines innetworked environments. Generally, program modules include routines,programs, objects, components, data structures, etc. that performparticular tasks or implement particular abstract data types.Machine-executable instructions, associated data structures, and programmodules represent examples of program code for executing steps of themethods disclosed herein. The particular sequence of such executableinstructions or associated data structures represent examples ofcorresponding acts for implementing the functions described in suchsteps.

Embodiments of the present invention may be practiced in a networkedenvironment using logical connections to one or more remote computershaving processors. Logical connections may include a local area network(LAN) and a wide area network (WAN) that are presented here by way ofexample and not limitation. Such networking environments are commonplacein office-wide or enterprise-wide computer networks, intranets and theInternet and may use a wide variety of different communicationprotocols. Those skilled in the art will appreciate that such networkcomputing environments will typically encompass many types of computersystem configurations, including personal computers, hand-held devices,multi-processor systems, microprocessor-based or programmable consumerelectronics, network PCs, minicomputers, mainframe computers, and thelike. Embodiments of the invention may also be practiced in distributedcomputing environments where tasks are performed by local and remoteprocessing devices that are linked (either by hardwired links, wirelesslinks, or by a combination of hardwired or wireless links) through acommunications network. In a distributed computing environment, programmodules may be located in both local and remote memory storage devices.

An exemplary system for implementing the overall system or portions ofthe invention might include a general purpose computing device in theform of a computer, including a processing unit, a system memory, and asystem bus that couples various system components including the systemmemory to the processing unit. The system memory may include read onlymemory (ROM) and random access memory (RAM). The computer may alsoinclude a magnetic hard disk drive for reading from and writing to amagnetic hard disk, a magnetic disk drive for reading from or writing toa removable magnetic disk, and an optical disk drive for reading from orwriting to a removable optical disk such as a CD ROM or other opticalmedia. The drives and their associated machine-readable media providenonvolatile storage of machine-executable instructions, data structures,program modules and other data for the computer.

The foregoing description of embodiments of the invention has beenpresented for purposes of illustration and description. It is notintended to be exhaustive or to limit the invention to the precise formdisclosed, and modifications and variations are possible in light of theabove teachings or may be acquired from practice of the invention. Theembodiments were chosen and described in order to explain the principalsof the invention and its practical application to enable one skilled inthe art to utilize the invention in various embodiments and with variousmodifications as are suited to the particular use contemplated.

Those skilled in the art will appreciate that the embodiments disclosedherein may be applied to the formation of any medical navigation system.Certain features of the embodiments of the claimed subject matter havebeen illustrated as described herein, however, many modifications,substitutions, changes and equivalents will now occur to those skilledin the art. Additionally, while several functional blocks and relationsbetween them have been described in detail, it is contemplated by thoseof skill in the art that several of the operations may be performedwithout the use of the others, or additional functions or relationshipsbetween functions may be established and still be in accordance with theclaimed subject matter. It is, therefore, to be understood that theappended claims are intended to cover all such modifications and changesas fall within the true spirit of the embodiments of the claimed subjectmatter.

1. A system for improved signal processing within a remote sensorsystem, the system including: a detection component adapted to detect anevent and generate a signal based at least in part on the event; and aprocessing component adapted to process a signal based at least in parton a situation.
 2. The system of claim 1, wherein the detectioncomponent includes at least one of a seismic detector, an acousticdetector, a magnetic detector, and a passive infra-red detector.
 3. Thesystem of claim 1, wherein the processing component is adapted todetermine a processing parameter based at least in part on thesituation.
 4. The system of claim 3, wherein the processing parameterincludes at least one of a filter bandwidth, a detection threshold, adetection pulse length, a detection pulse spacing, a detection timewindow length, a detection time window pulse number, an associationwindow length, and an association window threshold.
 5. The system ofclaim 1, wherein the situation includes at least one of a target type,an environment, and a dynamic environmental condition.
 6. The system ofclaim 1, wherein the situation is determined by a user.
 7. The system ofclaim 1, wherein the situation is determined automatically by thesystem.
 8. The system of claim 1, further including a classificationcomponent adapted to classify the event based at least in part on thesituation.
 9. The system of claim 8, wherein the classificationcomponent is adapted to determine a classification parameter based atleast in part on the situation.
 10. The system of claim 9, wherein theclassification parameter includes at least one of feature selection andfeature execution order.
 11. The system of claim 8, wherein theclassification component is adapted to select a feature set based atleast in part on the situation.
 12. The system of claim 1, furtherincluding an analysis component is adapted to analyze the signal.
 13. Amethod for improved signal processing within a remote sensor system, themethod including: determining a situation; detecting an event;generating a signal based at least in part on the event; and processingthe signal based at least in part on the situational parameter.
 14. Themethod of claim 13, wherein the processing step includes determining aprocessing parameter based at least in part on the situation.
 15. Themethod of claim 14, wherein the processing parameter includes at leastone of a filter bandwidth, a detection threshold, a detection pulselength, a detection pulse spacing, a detection time window length, adetection time window pulse number, an association window length, and anassociation window threshold.
 16. The method of claim 13, wherein thesituation includes at least one of a target type, an environment, and adynamic environmental condition.
 17. The method of claim 1, wherein thesituation is determined by a user.
 18. The method of claim 1, whereinthe situation is determined automatically.
 19. The method of claim 13,further including classifying the event based at least in part on thesituation.
 20. The method of claim 19, wherein the classifying stepincludes determining a classification parameter based at least in parton the situation.
 21. The method of claim 20, wherein the classificationparameter includes at least one of feature selection and featureexecution order.
 22. The method of claim 19, wherein classifying stepincludes selecting a feature set based at least in part on thesituation.
 23. The method of claim 13, further including analyzing thesignal.
 24. A computer readable storage medium including a set ofinstructions for execution on a computer, the set of instructionsincluding: a detection routine configured to detect an event andgenerate a signal based at least in part on the event; and a processingroutine configured to process the signal based at least in part on asituation.